Dense retrieval methods can effectively address these issues. The use of dense vector representations for retrieval has a long history since Latent Semantic Analysis [1]}. Recently, dense retrieval methods that are based on deep learning models trained on labeled query-document pairs have become popular [2]}, [3]}, [4]}. This section describes the dual-encoder architecture which is widely used for neural dense retrieval models (Section REF ), how dense retrieval models are applied efficiently to retrieve relevant documents over a large document collection using approximate nearest neighbor search (Section REF ), and the way dense retrieval models are trained using search logs (Section REF ).
